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Automating Real-Time data Analysis with Machine Learning algorithms

Automating Real-Time Data Analysis with Machine Learning Alg...

引用

Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Sonali P. Bhoite Satendra Singh Preeti Naval Panduranga Rao M V Bharat Bhushan G. Indira Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Device getting-to-know algorithms enable the automation of actual-time information analysis, permitting agencies to behave on well-timed insights and optimize selection-making. By leveraging predictive models, companies can increase customized services, optimize operations, and obtain a competitive area. This paper discusses how device learning algorithms can automate real-time facts evaluation and the ways such algorithms can be used to uncover developments, locate anomalies, and enhance models. Moreover, the paper explores how applying device mastering algorithms can boost accuracy and efficiency in data evaluation, in addition to providing businesses with new opportunities for innovation.

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A Comparative Study on the Effectiveness of Various Machine Learning Paradigms in Image Recognition and Predictive Modelling

A Comparative Study on the Effectiveness of Various Machine ...

引用

Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Deepak Upadhyay Mridul Gupta Aditya Verma Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

The efficiency of several machine learning algorithms in picture identification and predictive modelling is thoroughly compared in this research article. The necessity to find the best algorithms for certain use cases and the rising need for reliable and effective image recognition systems across a variety of areas are the driving forces behind the study. In order to assess how well these algorithms, perform in terms of accuracy, precision, and recall, the study uses a wide range of machine learning techniques, including classical and deep learning approaches. The study's findings show that convolutional neural networks, in particular, outperform conventional algorithms in image identification tests while standard and deep learning techniques perform similarly in tasks requiring predictive modelling. The study also emphasises the value of transfer learning and fine-tuning strategies in obtaining cutting-edge performance on picture datasets, and finally we will see results at the end.

关键词： Deep learning Machine learning algorithms Image recognition Transfer learning Predictive models Prediction algorithms data models

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Security Vulnerability and Key Points of Encryption Technology of Computer Information Technology data

Security Vulnerability and Key Points of Encryption Technolo...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Min Sun Wuhan Institute of Design and Sciences Wuhan China

With the continuous development of computer information technology, data storage and processing methods have changed, which has a great impact on information security. This paper describes the work done in this field at home and abroad. In combination with the emergence of new technologies such as network technology and cloud computing in the current era, it provides us with convenient conditions. At the same time, it also discusses how to apply these new science and technology to reality, and puts forward corresponding measures for reference and suggestions. This paper first introduces the data storage and processing methods of computer information technology and related concepts, and then briefly describes and analyzes computer information technology and information security. An encryption technology model for security vulnerabilities is designed to test the encryption technology performance of the model. The test results show that when the key is public key encryption, the running time is less than that of private key encryption, which means that with the increase of the number of files, the computational complexity increases at a relatively fast linear speed.

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What to predict from Twitter data?

What to predict from Twitter Data?

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Mohammed Salemdeeb Shaaban Sahmoud Electrical-Electronics Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Computer Engineering Department Fatih Sultan Mehmet Vakıf University Istanbul Turkey

In the last decade, Twitter data has become one of the most valuable research sources for many areas including health, marketing, security, and politics. Researchers prefer Twitter data since it is completely public and can be easily downloaded using Twitter APIs. The recent intensive use of Twitter data makes it difficult for researchers to follow or analyze its research. In this paper, we summarize most of the predictable patterns, aspects, and attitudes from Twitter data and analyze the performance and feasibility of the algorithms used. Moreover, we describe the current popular Twitter datasets used in various domains and applications. Current challenges and research gaps are discussed, and some recommendations are given for future works from different perspectives.

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IoV data processing algorithms for Automatic Real-Time Object Detection - Literature Review

IoV Data Processing Algorithms for Automatic Real-Time Objec...

引用

international conference on Inventive Computation Technologies (ICICT)

作者： Seghir Zineb Guezouli Lyamine Barka Kamel Assem Rezki Computer Science Department LaSTIC Laboratory Batna Algeria Renewable Energies and New Technologies Department LEREESI Laboratory Higher National School of Renewable Energies Environment & Sustainable Development Batna Algeria

With the advent of the Internet of Vehicles (loV)., there is an increasing demand for real-time and accurate object detection algorithms for intelligent transportation systems. This literature review aims to provide a comprehensive analysis of the latest research in loV data processing algorithms for automatic real-time object detection. By analyzing and comparing various state-of-the-art algorithms such as Faster R-CNN., Mask R-CNN., and YOLOv4., the strengths and limitations of each approach are identified. Additionally., the challenges associated with real-world implementation are discussed and provide recommendations for future research directions. This research study highlights the importance of efficient and accurate object detection algorithms in the context of the loV and emphasizes the need for ongoing research in this field.

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Classification of Event Sequences Based on Temporal Relation Features

Classification of Event Sequences Based on Temporal Relation...

引用

international conference on Big data and Smart computing (BIGCOMP)

作者： Kai Cheng Graduate School of Information Science Kyushu Sangyo University Fukuoka Japan

Temporal data include not only time-stamped raw data but also time intervals for events with a non-zero duration. Classification of interval-based event sequences has been an active research topic in the data science community. One major research issue in sequence classification is to extract discriminative features that properly capture the underlying sequences for high accuracy classification. Previous research on sequence classification mainly focused on time series using time-point based features. In this paper, we propose to define features based on Allen’s temporal relations between time intervals. Based on our earlier work on temporal data modeling, we develop a novel scheme for sequence representation of event-intervals. We describe the detailed algorithms and report the experimental results.

关键词： Training databases Time series analysis Semantics Symbols Syntactics data science

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Technical Analysis of Stock Market using data Science and its tools

Technical Analysis of Stock Market using Data Science and it...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Shrawani Pagar Aditya Jaiswal Atharva Auti Vansh Purohit Vivek Mishra Department of Cybersecurity Shah and Anchor Kutchhi Engineering College Mumbai India Department of Computer Science Shah and Anchor Kutchhi Engineering College Mumbai India

This paper presents a technical analysis approach for the stock market using data science and its associated tools. The objective is to develop a systematic framework that leverages data science techniques to analyze stock market data and make informed investment decisions. The proposed methodology involves data collection, preprocessing, feature engineering, and the application of various machine learning algorithms for predicting stock prices and identifying potential trading opportunities. The study demonstrates the effectiveness of data science in enhancing traditional technical analysis methods and provides insights into the potential benefits of integrating data-driven approaches into stock market analysis. Experimental results on historical stock data validate the proposed approach's accuracy and highlight its potential for improving investment strategies.

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Research on key technologies of high precision post processing kinematics based on multi system and multi frequency GNSS data

Research on key technologies of high precision post processi...

引用

Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Cong Li Xinjian Wang Yajun Wang Tianjin Transportation Technical College Tianjin China Qianxun Spatial Intelligence Inc Shanghai China

This paper introduces a high-precision post-processing kinematics method of Beidou includes GPS/ GLONASS/QZSS/Galileo with independent intellectual property rights. As the deployment of Beidou navigation satellite system (BDS) constellation completed, the increase of the number of satellites is conducive to improving the accuracy and reliability of high-precision post-processing kinematics method. In order to solve the problem that the ambiguity fixed rate is low due to the increase of ambiguity dimension, observation noise and atmospheric residual error, the integrity judgment of satellite is added in this method, the integer linear relationship between ambiguity of multi frequency carrier phase which is not affected by error is used to improve the ambiguity candidate value The strategy of row constraint and determination, and partial ambiguity fixed. The results show that the method can effectively improve the positioning accuracy.

关键词： Global navigation satellite system Time-frequency analysis Satellites Roads Kinematics Intellectual property User experience

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Application of Improved PilotNet Intelligent Traffic Algorithm Based on Baidu PaddlePaddle

Application of Improved PilotNet Intelligent Traffic Algorit...

引用

IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Huifang He Tingting Zhuang School of Information Engineering Guangdong Engineering Polytechnic Guangzhou China School of Data Science and Engineering Xingzhi College South China Normal University Shanwei China

In view of the complexity of current traffic scenes, the rapid development of intelligent transportation applications and the improvement of domestic platforms, this paper combines the current mainstream deep learning algorithms to recognize the road line with the PilotNet model improved by BatchNorm and Dropout technologies under the Baidu PaddlePaddle framework, and to detect the traffic signs with the YOLOv3 Tiny model. Due to the limited hardware system resources and the real-time nature of automatic driving, finally, the two types of models are deployed to the Edge Board computing box. In the application of intelligent transportation, single-thread technology cannot respond to the real-time nature of automatic driving, and thread blocking has a great impact. After comparison, dual-thread technology is adopted. data processing is carried out by capturing the moving image of the car and the control value, and the experiment is completed. In the experiments, the smart car can recognize the traffic signs at a speed of around 0.01s per frame and finish model loading and making decision after the recognition of traffic scenes in 34.86s, which shows that the trained neural network models all have lightweight characteristics and can meet the system application with limited resources.

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Automation of data Transmission for Wallet With Novel Merchant Payment System Using Cluster Classification Over Sequence Analysis Method

Automation of Data Transmission for Wallet With Novel Mercha...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Aishwary Awasthi Richa Mehta Mohit Rathod Hayder Mahmood Salman Bechoo Lal Badria SulaimanAlfurhood Department of Mechanical Engineering Sanskriti University Mathura Uttar Pradesh India Department of ECE Chandigarh Engineering College Jhanjeri India Department of CSE PIT Parul University Vadodara Gujarat India Computer Science Department Al-TurathUniversiy College Baghdad Iraq Department of CSE KoneruLakshmaiah Education Foundation (KLEF) KL Deemed University Vijayawada Campus AP India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia

To improvise a data transmission for wallet with merchant payment system using cluster classification over sequence analysis method is proposed in this paper. The implementation have been carried out for processing the data through MySQL and the algorithms are tested with Net Beans (IDE) applications. The cluster classification paved the way to achieve the enhancement of data transmission of data. With the sample size of 46 and tested around 11 number of times compared with the sequence analysis method. The speed with the accuracy of 92% for data transmission for wallets with merchant payment systems using cluster classification, provides significantly better results compared to sequence analysis methods. There was a statistical significance between cluster classification and sequence analysis *** improvise a data transmission for a wallet with a merchant payment system using cluster classification over sequence analysis method provides more significant accuracy of 94.4 percent than sequence analysis method by analyzing the parameters of time with data and received time.

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